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oai:drops-oai.dagstuhl.de:3723 2024-03-06T10:29:05Z ddc:004 open_access

Unbiased Protein Interface Prediction Based on Ligand Diversity Quantification Esmaielbeiki, Reyhaneh Nebel, Jean-Christophe Protein-protein interaction protein interface prediction homology modeling Proteins interact with each other to perform essential functions in cells. Consequently, identification of their binding interfaces can provide key information for drug design. Here, we introduce Weighted Protein Interface Prediction (WePIP), an original framework which predicts protein interfaces from homologous complexes. WePIP takes advantage of a novel weighted score which is not only based on structural neighbours' information but, unlike current state-of-the-art methods, also takes into consideration the nature of their interaction partners. Experimental validation demonstrates that our weighted schema significantly improves prediction performance. In particular, we have established a major contribution to ligand diversity quantification. Moreover, application of our framework on a standard dataset shows WePIP performance compares favourably with other state of the art methods. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Reyhaneh Esmaielbeiki and Jean-Christophe Nebel 2012 Is Part Of OASIcs, Volume 26, German Conference on Bioinformatics 2012 InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.GCB.2012.119 urn:nbn:de:0030-drops-37235 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.119 eng https://creativecommons.org/licenses/by-nd/3.0/legalcode